In theoretical perspective of ML, predicting material property is straightforward.
Here, we are looking for a function \(F\)  that takes material features as input, and outputs the property of interest.
The input should contain features that reflect observable characteristics of the materials.
There are a variety of features and functions used in existing research[1]} .
In this article, the input contains a feature descriptor and the position of each atom in the materials.
The parameters of the function are determined using properties of interest of known materials.
The neural network is expected to have great generalization capabilities and thus, could effectively predict the property of interest for new materials.
